Automated detection of objects within digital images is a technology that has many benefits. For example, automated face detection is useful in access control, surveillance, and security operations, among others. Automated object-detection technology has progressed significantly as computing power has been increased over the years, allowing for faster and faster execution of complex algorithms. Along with increases in processing power has come improvements to object-detection processing architectures.
For example, robust face detection in the wild is one of the ultimate components for supporting various facial related problems, such as unconstrained face recognition, facial periocular recognition, facial landmarking and pose estimation, facial expression recognition, and 3D facial model construction, among others. Although the face-detection problem has been intensely studied for decades, resulting in various commercial applications, it still meets problems in some real-world scenarios due to numerous challenges, including heavy facial occlusions, extremely low resolutions, strong illumination, exceptional pose variations, image or video compression artifacts, etc.